Net terrestrial CO2 exchange over China during 20012010

PUBLICATIONS
Journal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/2013JD021297
Key Points:
• Chinese terrestrial CO2 ﬂux were estimated by CTDAS
• Most Chinese land ecosystems was CO2
sinks with large interannual variability
• More atmospheric CO2 observations
within and surrounding China are needed
Supporting Information:
• Readme
• Table S1
Correspondence to:
B. Z. Chen and J. Chen,
[email protected];
[email protected]
Citation:
Zhang, H. F., B. Z. Chen, I. T. van der
Laan-Luijkx, J. Chen, G. Xu, J. W. Yan, L. X.
Zhou, Y. Fukuyama, P. P. Tans, and W.
Peters (2014), Net terrestrial CO2
exchange over China during 2001–2010
estimated with an ensemble data
assimilation system for atmospheric
CO2, J. Geophys. Res. Atmos., 119,
3500–3515, doi:10.1002/2013JD021297.
Received 3 DEC 2013
Accepted 18 FEB 2014
Accepted article online 23 FEB 2014
Published online 20 MAR 2014
Net terrestrial CO2 exchange over China during
2001–2010 estimated with an ensemble data
assimilation system for atmospheric CO2
H. F. Zhang1,2, B. Z. Chen1, I. T. van der Laan-Luijkx3, J. Chen1, G. Xu1,2, J. W. Yan1,2, L. X. Zhou4,
Y. Fukuyama5, P. P. Tans6, and W. Peters3,7
1
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural
Resources Research, Chinese Academy of Sciences, Beijing, China, 2University of Chinese Academy of Sciences, Beijing,
China, 3Department of Meteorology and Air Quality, Wageningen University, Wageningen, Netherlands, 4Key Laboratory
for Atmospheric Chemistry of China Meteorological Administration, Research Institute of Atmospheric Composition of
Chinese Academy of Meteorological Sciences, Beijing, China, 5Atmospheric Environment Division, Japan Meteorological
Agency, Tokyo, Japan, 6Earth System Research Laboratory, National Oceanographic and Atmospheric Administration,
Boulder, Colorado, USA, 7Centre for Isotope Research, University of Groningen, Groningen, Netherlands
Abstract In this paper we present an estimate of net ecosystem CO2 exchange over China for the years
2001–2010 using the CarbonTracker Data Assimilation System for CO2 (CTDAS). Additional Chinese and
Asian CO2 observations are used in CTDAS to improve our estimate. We found that the combined terrestrial
ecosystems in China absorbed about 0.33 Pg C yr1 during 2001–2010. The uncertainty on Chinese
terrestrial carbon exchange estimates as derived from a set of sensitivity experiments suggests a range of
0.29 to 0.64 Pg C yr1. This total Chinese terrestrial CO2 sink is attributed to the three major biomes
(forests, croplands, and grass/shrublands) with estimated CO2 ﬂuxes of 0.12 Pg C yr1 (range from 0.09 to
0.19 Pg C yr1), 0.12 Pg C yr1 (range from 0.09 to 0.26 Pg C yr1), and 0.09 Pg C yr1 (range from
0.09 to 0.17 Pg C yr1), respectively. The peak-to-peak amplitude of interannual variability of the Chinese
terrestrial ecosystem carbon ﬂux is 0.21 Pg C yr1 (~64% of mean annual average), with the smallest CO2 sink
(0.19 Pg C yr1) in 2003 and the largest CO2 sink (0.40 Pg C yr1) in 2007. We stress that our estimate of
terrestrial ecosystem CO2 uptake based on inverse modeling strongly depends on a limited number of
atmospheric CO2 observations used. More observations in China speciﬁcally and in Asia in general are
needed to improve the accuracy of terrestrial carbon budgeting for this region.
1. Introduction
Terrestrial ecosystems play a critically important role in determining the global atmospheric CO2 concentration
and in future atmospheric CO2 levels. Global terrestrial ecosystems absorbed about 2–4 Pg C per year during
2001–2011, offsetting close to 30% of the anthropogenic emissions [Houghton, 2007; Le Quéré et al., 2009,
2013]. Variations in the terrestrial ecosystem carbon uptake were mainly responsible for the varying growth
rate of CO2 in the atmosphere [Houghton, 2007; Le Quéré et al., 2009; Saeki et al., 2013]. However, the
magnitude and spatial and temporal distributions of these terrestrial carbon sinks and sources remain
uncertain [Cao et al., 2003b; Tian et al., 2011].
The importance of Chinese terrestrial ecosystems in the global carbon cycle has been increasingly
recognized. Previous studies suggested that China contributed substantially to the uptake of carbon [Cao
et al., 2003a; Fang et al., 2007; Piao et al., 2009; Wang et al., 2011a], as well as to carbon emissions [Gregg et al.,
2008; Guan et al., 2009; Peters et al., 2011]. It is estimated that Chinese ecosystems absorbed 0.26 Pg C yr1
CO2 during the period 1988–2001 [Piao et al., 2009] and China contributed 1.43 Pg C yr1 of CO2 emission
into the atmosphere from fossil fuel combustion during the period 2000–2010 [Boden et al., 2011]. As China is
the largest emitter [Boden et al., 2011; Le Quéré et al., 2009; Piao et al., 2009] of fossil fuel CO2 into the
atmosphere from 2006 onward in the world due to economic growth and increasing energy consumption,
there is a growing scientiﬁc and political interest to better understand the Chinese terrestrial carbon balance
[Houghton, 2007; Piao et al., 2009]. Quantifying the carbon ﬂux in China is therefore essential both for
understanding the global and regional carbon balance and for accurate assessment of the carbon distribution
of Chinese terrestrial ecosystem.
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
3500
Journal of Geophysical Research: Atmospheres
10.1002/2013JD021297
Several approaches have been applied to
quantify Chinese terrestrial CO2 exchange
over recent decades, including many
“bottom-up” studies [Cao et al., 2003a,
2003b; Fan et al., 2012; Fang et al., 2007; Liu
et al., 2012; Lun et al., 2012; Tian et al., 2011;
Yu et al., 2013] and only a few “top-down”
[Berezin et al., 2013; Jiang et al., 2013; Piao
et al., 2009]. Atmospheric inversion, as one
of the top-down approaches, is an effective
way to trace spatiotemporal variations of
CO2 sources and sinks. It has been well
Figure 1. A map of the surface observation sites from the Global developed and widely applied to estimate
Cooperative Air Sampling Network (NOAA-ESRL) and from sites
the global and regional carbon ﬂuxes [Baker
whose data are included in the online database of WDCGG. Black
et al., 2006; Chevallier and O’Dell, 2013; Deng
circles represent the Chinese and nearby Asian CO2 measurements
et al., 2007; Gurney et al., 2003, 2004].
typically used in CarbonTracker, while the red circles denote the
additional observations included in this study. Note that the station of Inverse modeling studies focusing on
RYO is not assimilated in the CTDAS and used for independent evaChina, however, started relatively late due
luation of our simulations.
to the lack of routine monitoring of
atmospheric CO2 mole fractions in the
region. Only recently, several wellcalibrated observations’ sites (i.e., Shangdianzi (SDZ), Longfengshang (LFS), Li’an (LAN), and Mount Waliguan
(WLG), see Figure 1 and Table 1) [Fang et al., 2013; Liu et al., 2009] were established and became operational in
China. Their locations were chosen to maximize the spatial coverage of surface observations and are
potentially very useful for inferring CO2 surface ﬂuxes over China. In addition, the expanded atmospheric CO2
observation network in other Asian countries (See Figure 1, e.g., Yonagunijima: YON, in Japan, 24.47°N,
123.02°E; Ryori: RYO, in Japan, 39.03°N, 141.82°E; Minamitorishima: MNM, in Japan, 24.29°N, 153.98° and
Gosan: GSN, in South Korea, 33.15°N, 126.12°E) is useful for improving Chinese terrestrial ﬂux estimates by
imposing extra constraints.
a
Table 1. Summary of the Chinese and Asian Surface CO2 Observation Data Assimilated Between 1 January 2001 and 31 December 2010
Site
Name
Discrete Samples
WLG
Waliguan, China
BKT
Bukit Kototabang, Indonesia
WIS
Sede Boker, Israel
KZD
Sary Taukum, Kazakhstan
KZM
Plateau Assy, Kazakhstan
TAP
Tae-ahn Peninsula, South Korea
UUM
Ulaan Uul, Mongolia
SDZ
Shangdianzi, China
LFS
Longfengshang, China
LAN
Lian, China
Continuous Samples
MNM
Minamitorishima, Japan
c
RYO
Ryori, Japan
YON
Yonagunijima, Japan
GSN
Gosan, South Korea
b
Lat, Lon, Elev.
Lab
Data Set Provider
N (Flagged)
MDM
Bias
36.29°N, 100.90°E, 3810 m
0.20°S, 100.312°E, 864 m
31.13°N, 34.88°E, 400 m
44.45°N, 77.57°E, 412 m
43.25°N, 77.88°E, 2519 m
36.73°N, 126.13°E, 20 m
44.45°N, 111.10°E, 914 m
40.39°N, 117.07°E, 293 m
24.47°N, 123.02°E, 30 m
33.15°N, 126.12°E, 72 m
CMA /ESRL
ESRL
ESRL
ESRL
ESRL
ESRL
ESRL
CMA /ESRL
CMA
CMA
b
NOAA-ESRL
NOAA-ESRL
NOAA-ESRL
NOAA-ESRL
NOAA-ESRL
NOAA-ESRL
NOAA-ESRL
[Cheng et al., 2013]
[Cheng et al., 2013]
[Cheng et al., 2013]
391(20)
246(0)
482(4)
384(23)
345(3)
342(1)
459(7)
152(8)
79(5)
146(5)
1.5
7.5
2.5
2.5
2.5
7.5
2.5
7.5
7.5
7.5
0.11
5.43
0.30
0.43
0.30
0.46
0.18
1.83
3.91
5.20
24.29°N, 153.98°E, 8 m
39.03°N, 141.82°E, 260 m
24.47°N, 123.02°E, 30 m
33.15°N, 126.12°E, 72 m
JMA
JMA
JMA
b
NIER
WDCGG
WDCGG
WDCGG
WDCGG
3309(0)
3309()
3317(11)
2537(236)
3
3
3
0.26
0.96
1.32
b
a
The frequency of continuous data is one time per day (when available), while discrete surface data are generally once per week. MDM (model data mismatch) is
a value assigned to a given site that is meant to quantify our expected ability to simulate observations. N denotes that the number is available in the CTDAS.
Flagged observations mean a model-minus-observation difference that exceeds 3 times of the model data mismatch and are therefore excluded from assimilation. The bias is the average from posterior residuals (ﬁnal modeled values-measured values). The additional observations from China and nearby Asia are
presented in bold type.
b
NOAA-ESRL: National Oceanic and Atmospheric Administration’s Earth System Research Laboratory; CMA: China Meteorological Administration; NIER: National
Institute of Environmental Research South Korea; JMA: Japan Meteorological Agency.
c
RYO: was not assimilated but used as independent assessment of our Chinese terrestrial CO2 ﬂux.
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
3501
Journal of Geophysical Research: Atmospheres
10.1002/2013JD021297
In this study, we used the CO2 data assimilation system and inverse model CarbonTracker Data Assimilation
System for CO2 (CTDAS) (http://carbontracker.eu/ctdas/) to quantify the weekly net ecosystem CO2 exchange
(NEE) in China during the period 2001–2010. Jiang et al. [2013] reported an estimation of Chinese ﬂux also using
the Chinese observations data (SDZ, LAN, LFS, and WLG). The major difference in inverse modeling design
between these two studies are threefolds: (1) different treatments to the CO2 concentration observation data: this
study directly used these weekly ﬂask data in the inversions without any processing, while Jiang et al. [2013] used
monthly mean values following the GLOBALVIEW data processing procedure; (2) different transport resolution: this
study uses a nested grid over China with 1 × 1° base on the parent horizontal resolutions of global 6 × 4° and Asia
3 × 2°, while Jiang et al. [2013] used a global 3 × 2° resolution without nested grids, and ﬁnally, (3) the inversion
methodology differed: Jiang et al. [2013] divided China into 13 small regions within the global TransCom 22 land
regions and inverted for monthly mean ﬂuxes using a large matrix approach, while we estimate 30 regions in
China on a weekly basis, using an ensemble Kalman ﬁlter technique.
This paper includes four sections. Materials and methods of inverse modeling are provided and described in
section 2. Section 3 presents the inverted Chinese CO2 ﬂux and its temporal-spatial characteristics. The
comparison of our inferred Chinese surface ﬂux with previous results is also included in this section. Finally,
we summarize our ﬁndings in section 4.
2. Materials and Methods
2.1. Outline of CTDAS
The inversion system CTDAS has been successfully applied to estimating global and regional carbon ﬂuxes,
especially in North America, Europe, and East Asia [Peters et al., 2007, 2010; Zhang et al., 2013]. We brieﬂy
described the system here; detailed information can be found in Peters et al. [2007, 2010] and Zhang et al.
[2013]. CTDAS uses the off-line atmospheric transport model TM5 [Krol et al., 2005] as a forward operator in an
ensemble ﬁxed-lag Kalman smoother [Peters et al., 2005]. This system was designed to estimate net terrestrial
and oceanic surface ﬂuxes by minimizing the Euclidean distance between the simulated and the observed
CO2 mole fraction using the cost function:
T
1
1
J ¼ y 0 Hðx Þ R1 y 0 Hðx Þ Þ þ ðx x 0 ÞT P1 ðx x 0 Þ
(1)
2
2
where y0 are the CO2 observations with error covariance matrices R, x0 is the vector of the a priori ﬂux with error
covariance matrices P, x denotes a vector of the net terrestrial and oceanic surface ﬂuxes to be estimated, and H is
the atmospheric transport model and observation operator that translates the ﬂux in the model space into the
observations space. Similar to Peters et al. [2007, 2010], the surface ﬂuxes can be further divided into four
categories as follows:
Neco
F ðx; y; tÞ ¼
Noce
oce
∑ λeco
r F bio ðx; y; t Þ þ ∑ λr F oce ðx; y; t Þ þ F ff ðx; y; t Þ þ F fire ðx; y; t Þ
r¼1
r¼1
(2)
where Fbio and Foce present a priori land biosphere and ocean ﬂuxes with 3-hourly, 1 × 1° resolution, Fff and
Fﬁre are prescribed ﬂuxes of fossil fuel combustion and ﬁre emissions with monthly 1 × 1° resolution, λr is a set
of 209 weekly scaling factors, and each scaling factor is associated with a particular ecosystem region of the
global domain based on its climate zone, continent, and land cover type derived from Olson et al. [1985].
Ocean ﬂuxes are similarly scaled across 30 large basins, as deﬁned in Jacobson et al. [2007]. Together, this
leads to a possible 239 scaling factors worldwide each week. The actual number assimilated in CTDAS is 156,
because a sizeable number (83) of scaling factors are associated with a nonexisting ecosystem (such as
“snowy conifers” in Africa), and 30 are directly relevant to China. The scaling factors λr are estimated and
optimized in the inversion, and the ﬁnal optimized λr together with Fbio, Foce, Fff, and Fﬁre determine the
optimized instantaneous CO2 ﬂuxes in CTDAS. Note that Fff and Fﬁre are not optimized in the inversion.
In this study, we have tailored the CTDAS for CO2 to the Chinese terrestrial carbon cycle, using weekly
resolution and 5 week lag windows as in Peylin et al. [2013]. The major modiﬁcations to the system are
summarized as follows: (1) the atmospheric transport model TM5 [Krol et al., 2005] used in CTDAS had a
global horizontal resolution of 6 × 4° with a nested grid of 3 × 2° over Asia and a further nested grid of 1 × 1°
over China; (2) three additional atmospheric CO2 observation sites in China as well as four sites in nearby Asia
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD021297
were added to the data assimilation system (details are presented in section 2.3); and (3) sensitivity
experiments were performed to investigate the effects of involving additional CO2 observations into the
inversion system on the Chinese ecosystem CO2 ﬂux estimates (details are presented in section 2.4).
2.2. Prior Fluxes and Meteorological Input Data
ERA-Interim data [Dee et al., 2011] from the European Centre for Medium-Range Weather Forecasts (ECMWF,
http://www.ecmwf.int/research/era/do/get/index) were used in CTDAS to drive the transport model. The four
surface ﬂux data sets (see equation (2)) used in our assimilation are as follows:
1. The ﬁrst guess (a priori) terrestrial biosphere exchange data were from the Carnegie-Ames-Stanford
approach (CASA)-GFED2 (Global Fire Emissions Database version 2, using the Carnegie-Ames Stanford
Approach) biogeochemical modeling system [Werf et al., 2006]. The monthly net biosphere ﬂuxes
(NEE = Re + GPP) were calculated from CASA gross primary production (GPP) and ecosystem respiration
(Re), and then interpolated into 3-hourly NEE ﬂux using a relation involving Q10 and incident solar radiation [Olsen and Randerson, 2004].
2. The a priori net ocean surface ﬂuxes were calculated based on air-sea CO2 partial pressure difference of
ocean interior inversion calculations [Jacobson et al., 2007]. These air-sea partial pressure differences were
combined with a gas transfer velocity computed from wind speeds in the atmospheric transport model to
compute ﬂuxes of carbon dioxide across the sea surface every 3 h.
3. The ﬁre emissions were acquired from the Global Fire Emission Database version 2 (GFED2, http://ess1.ess.
uci.edu/jranders/data/GFED2), which were integrated from the CASA biogeochemical model. In this system, ﬁres consume biomass from different carbon pools simulated by the CASA model, establishing a coupling between satellites observed burned area, deforestation, and regrowth of seasonally burning
vegetation in, for instance, savannah areas.
4. The global fossil fuel emission inventory data were from the so-called “Miller ﬂuxes” (J. B. Miller, personal
communication, 2010), which were ﬁrst constructed based on country total fossil fuel emissions from the
Carbon Dioxide Information and Analysis Center (CDIAC) [Marland et al., 2003] and global average total
fossil fuel combustion (http://cdiac.ornl.gov/trends/emis/meth_reg.html) and then scaled to 1 × 1° resolution based on the EDGAR (Emission Database for Global Atmospheric Research) database [Boden et al.,
2011; Commission, 2009; Thoning et al., 1989]. This approach is identical to Peters et al. [2007, 2010] with
detailed information documented at (http://carbontracker.noaa.gov/documentation).
Note that for 2009 and 2010, climatological averages were used for biomass burning a priori ﬂuxes and for
the seasonal cycle of NEE in the biosphere. This is necessary because of a change in processing of the parent
products (GFED2 and CASA normalized difference vegetation index (NDVI)), and previous tests for North
America and Europe (CarbonTracker Europe, unpublished manuscript, 2013) have shown the impact of this
climatological approach to be minimal except for the tropical biomass burning areas; we therefore expect the
same for China. Our inversion approach and the treatment of uncertainty on NEE and ocean ﬂuxes are similar
to Peters et al. [2007, 2010], Zhang et al. [2013], and Peylin et al. [2013] and also documented at (http://www.
esrl.noaa.gov/gmd/ccgg/carbontracker/CT2010/documentation).
2.3. CO2 Observations and Model Data Mismatch
Atmospheric CO2 observations from a wide range of experimental sites operated by different laboratories
around the world were used in this study. We obtained the data from ObsPack (version 1.0.2) distributed
through National Oceanic and Atmospheric Administration-Earth System Research Laboratory (NOAA-ESRL)
(http://www.esrl.noaa.gov/gmd/ccgg/obspack/) and by the WDCGG (World Data Centre for Greenhouse
Gases, http://ds.data.jma.go.jp/gmd/wdcgg/). A list of all of the sites used in our system was shown in Table
S1 of the supporting information (SI). The CO2 observation sites used in this study were different from those
used in CarbonTracker Europe [Peters et al., 2010]: (1) besides WLG, three more sites in China (SDZ, LFS, and
LAN, coverage period: July 2006 to January 2010) and (2) four more stations (Minamitorishima (MNM),
Yonagunijima (YON), Gosan (GSN), and Ryori (RYO)) in nearby Asian countries were added. Note that Ryori
(RYO) from the Japan Meteorological Agency (JMA) was not assimilated but used for independent
assessment of our Chinese terrestrial CO2 ﬂux. The Chinese and nearby Asian surface observation sites used
in this study are summarized in Table 1 and Figure 1.
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD021297
Following Peters et al. [2007, 2010], we chose the local afternoon (12:00–16:00) average mole fraction data as
our model inputs for most of the continuous sampling sites for each day, while for mountaintop sites, such
as Mauna Loa (MLO, in Hawaii, United States, 19.54°N, 155.58°E), we used average mole fraction data of the
local nighttime hours (0:00–4:00). This data selection strategy recognizes that the atmospheric transport
model is better able to match average mole fractions for well-mixed conditions, which occur generally
during daytime for most sites and during nighttime for elevated sites.
Model data mismatch (MDM) is an important term in data assimilation and is used to determine the expected
degree to which simulated CO2 mole fraction will match the observed data [Gurney et al., 2002, 2003]. Many
studies have used various different ways to describe the MDM [Deng et al., 2007; Michalak et al., 2005; Saeki
et al., 2013]. In this study the model data mismatch is the random error ascribed to each observation to
account for measurement errors and modeling errors of that observation. Following Peters et al. [2005], the
MDM in CTDAS is classiﬁed into six categories: (1) marine boundary layer (MDM = 0.75 ppm); (2) land stations
(MDM = 2.50 ppm); (3) mixed stations (MDM = 1.50 ppm); (4) aircraft measurements (MDM = 2.00 ppm); and
(5) tower stations (MDM = 3.00 ppm). The sixth category, MDM = 7.5 ppm, is used for sites which are most
difﬁcult. Similar to Peters et al. [2005], we discard observations in categories 2–6 in our assimilation when the
residuals (observed-simulated) exceed 3 times MDM. These MDM values represent subjective choices meant
to reﬂect small instrumental errors and large modeling errors related to the representative of each site and
model resolution. They are not based on an optimization or analysis of representation errors in our model.
The MDM of Chinese and nearby Asian surface CO2 observation used in this study is shown in Table 1.
2.4. Sensitivity Experiments
In this study we designed two modeling sensitivity experiments to explore the sensitivity of the estimated
terrestrial CO2 ﬂuxes in China.
Case 1: Only one site (WLG) in China and six sites (KZM, KZD, UUM, BKT, WIS, and TAP) in nearby Asian countries were included in CTDAS. We used these results (quotes as Case 1) to examine the impact of involving
additional CO2 observations in the assimilation system on Chinese ﬂux estimates by comparing with Case 2.
Case 2: Same as Case 1 but with additional observations from three ﬂask stations in China (SDZ, LFS, and
LAN) and three continuous sites in nearby Asia (MNM, YON, and GSN). Case 2 is expected to yield more
reliable ﬂux estimates which are used to analyze the 10 year mean carbon balance in this study.
Except for the CO2 observations used, we keep all other modeling sets (e.g., prior ﬂuxes, meteorological
driving data, and spatiotemporal resolution) to be the same in these two sensitivity experiments (Cases 1 and
2). The simulations spanned the period from 2000 to 2010, and the year 2000 was used as a spin-up year to
initialize the model and was therefore excluded from the analyses.
In addition to using different observation sets in these two cases above, four simulations were used to
investigate the model uncertainty:
Case 3: Same as Case 2, but the model runs at uniform global 6° × 4° grid without any zoom. We use these
results to test the effect of spatial resolution in the inversion system.
Case 4: Same as Case 2, but prior biosphere ﬂuxes were now derived from the Simple Biosphere/CarnegieAmes-Stanford Approach (SIBCASA) land surface model [Schaefer et al., 2008; I. van der Velde et al., New
developments in SiBCASA: Terrestrial 13 C exchange and biomass burning, 2013]. Note that the biomass
burning emissions were still from the CASA-GFED2. We use these results to investigate the impact of the prior
land ﬂux on posterior ﬂuxes.
Case 5: Same as Case 2, but the model runs with 3 weeks of scaling factors in the state vector, creating a
shorter smoothing window for the ﬁxed-lag ﬁlter [Bruhwiler et al., 2005]. We use these results to test the effect
of smoother window length on the inferred surface ﬂuxes.
Case 6: Same as Case 2, but the model runs with doubled MDM values. We use these results to check the
effect of MDM on the inferred surface ﬂuxes.
These four simulations (Cases 3–6) only spanned the period 2008–2010 for sensitivity tests, and no detailed
discussion was included in this paper. The results are summarized in Table 2, and their values are used to
estimate the uncertainty range. Note that this range is not a Gaussian uncertainty but rather a substitute for
the formal covariance estimate of the ensemble Kalman ﬁlter, which in CTDAS does not contain the (large)
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
3504
Journal of Geophysical Research: Atmospheres
Table 2. Results of the Sensitivity Experiments
a
1
Conducted in This Study (Pg C yr )
Sensitivity Experiments
1
Post. Fluxes (P g C yr
)
0.29
0.33
0.64
0.37
0.37
0.35
Case 1
Case 2
Case 3
Case 4
Case 5
Case 6
10.1002/2013JD021297
component of temporal covariance and is therefore
unsuitable to estimate annual or long-term mean
uncertainty [Peters et al., 2007, 2010, 2005].
3. Results and Discussion
3.1. Model Evaluation With
Independent Observations
First, we checked the accuracy of modeled CO2 concentrations
at the measurement locations. Figure 2 compares
Cases 1 and 2 span the period 2000–2010, while
Cases 3–6 only run from 2008 to 2010.
simulated and observed CO2 concentrations at the
nonassimilated RYO station during 2001–2010. The
observed background CO2 concentrations were well captured by the model (Figures 2a and 2b). The
amplitudes of observed CO2 mole fractions were well ﬁt by the model, and the seasonal estimates have
accurate timing in spring (March–April–May) and autumn (September–October–November) but sometimes
slightly underestimate in winter (December–January–February) and overestimate summer (June–July–August).
The mean difference is 0.36 ± 2.19 ppm with a relatively large bias of 0.98 ± 3.07 ppm in summer (model
overestimates the observed CO2 mole fraction) and a slight bias of 0.12 ± 1.44 ppm in winter (model
underestimates observed CO2). This suggests that our estimated CO2 surface ﬂuxes may not catch the lowest
annual values due to the major terrestrial carbon uptake that occurred in summer. Previous studies have also
found this seasonal mismatch, which may correlate with the atmospheric transport model, and have already
been identiﬁed as shortcomings in inverse modeling systems [Peylin et al., 2013; Stephens et al., 2007; Yang et al.,
2007]. Overall, the agreement between the observations and model simulations is fairly good and shows
no evidence of large biases in the combined ﬂuxes and transport in CTDAS over China. In addition, the
a
405
405
(a)
400
400
Simulated CO2 (ppm)
395
CO2 (ppm)
390
385
380
375
370
365
Observed
Simulated
360
355
2002
2004
2006
2008
390
385
380
370
365
360
(c)
40
360
370
380
390
400
Observed CO2 concentration (ppm)
time series (year)
45
y=0.95X+19.22
R2=0.92
375
355
2010
(b)
395
100
NH summer,Jun−Aug
(d)
NH winter,Dec−Feb
80
35
30
60
25
20
40
0.98 ± 3.07
n = 594
15
−0.12 ± 1.44
n = 775
10
20
5
0
−10
−5
0
5
10
ΔCO2 = Simulated − Observed,(ppm)
0
−4
−2
0
2
4
ΔCO2 = Simulated − Observed,(ppm)
Figure 2. (a) Time series of CO2 mole fractions at RYO station, both simulated and observed, (b) linear regression of
observed CO2 and simulated CO2, (c) summer histograms of the residuals, and (d) winter histograms of the residuals
during 2001–2010. The blue columns in Figures 2c and 2d show the histogram of the residuals, and the blue lines and
statistics shown in blue text (mean, standard deviation, and observed number, respectively) are a summary of the residuals
interpreted as a normal distribution. The vertical scales are determined by the number of observations and how tightly they
are grouped, with the area under the histogram forced to unity.
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
3505
Journal of Geophysical Research: Atmospheres
10.1002/2013JD021297
performance of CarbonTracker to simulate
vertical proﬁles of CO2 mole fractions has
already been assessed in previous research.
For example, Peters et al. [2007] compared
the optimized CO2 concentration with
13,000 independent ﬂask measurements in
the free troposphere (FT). The comparison
results show that the agreement with FT
observations at the level of a few tenths
(0.07 ± 1.91 ppm) of a parts per million,
which is quite satisfactory and presents no
evidence of large biases in the transport of
CarbonTracker. For Asia, this was conﬁrmed
by a comparison to CONTRAIL aircraft data
in Zhang et al. [2013].
(a)
(b)
3.2. Estimated Chinese Terrestrial
CO2 Flux
3.2.1. Ten Year Mean CO2 Flux
In the rest of the paper, we choose to use a
negative sign for CO2 sinks and a positive
sign for a CO2 source. We found that the
Figure 3. (a) Mean terrestrial biosphere CO2 ﬂux in China during 2001– Chinese terrestrial ecosystems absorbed
2010. Blue colors (negative) denote net carbon uptake while red colors
an average of 0.33 Pg C yr1 during the
(positive) denote carbon release to the atmosphere. Note that the
period 2001–2010. This represents a net
estimated ﬂux map includes net terrestrial uptake and biomass burnterrestrial sink of CO2 including biomass
ing emissions but excludes fossil fuel emissions. (b) Map of the ecoreburning emission (+0.02 Pg C yr1) but
gion types over China from Olson et al. [1985] with 19 land cover
excluding fossil fuel emission (+1.70 Pg C
classes used in this study, along with nine regions in China: northeast
China, Inner Mongolia, northwest China, north China, Tibetan Plateau, yr1). Uncertainties in the estimated
southwest China, central China, southeast China, and south China.
terrestrial CO2 uptake were assessed
through a set of sensitivity experiments
(Cases 1–6 and Table 2) ranging from 0.29 to 0.64 Pg C yr1. Note that this range is much smaller (0.29 to
0.38 Pg C yr1 when excluding Case 3 with the lower model resolution run, which poorly captures the Chinese
sites near urban centers, degrading its performance). This uncertainty estimate complements the Gaussian
uncertainty estimate (± 0.36 Pg C yr1, this is the average weekly background covariance over the whole period
of 2001–2010) and further reﬂects the natural uncertainty of estimated annual mean ﬂuxes [Peters et al., 2007,
2010]. The uncertainties in fossil fuel and biomass burning emission data set used in the inversion system could
have an inﬂuence on our estimated terrestrial net CO2 ﬂux as well [see, for instance, Francey et al., 2013]. We
investigated this uncertainty by comparing the inverted results using two different fossil fuel emission data sets
acquired from “Miller” and from Wang et al. [2012] and found that the effect of this uncertainty related to the
fossil fuel emissions on the estimated terrestrial CO2 ﬂuxes of China was mostly inﬂuencing the spatiotemporal
variations but not the mean ﬂuxes reported here.
Figure 3a shows the 10 year mean spatial distribution of the estimated Chinese terrestrial CO2 ﬂux. It is clear
that most of China’s ecosystems were carbon sinks, with an especially strong CO2 uptake in northeastern
China. Large carbon sinks were also found in north China, central China, southeast China, and southwest
China, whereas the sinks in northwest China, the Tibetan Plateau, and south China were relatively smaller.
Large sinks were found in the three major land cover types: forests, crops, and grass. As shown in Figures 3a
and 3b and Table 3, most of the uptake by the forests occurred in conifer forests (0.04 Pg C yr1), broadleaf
forests (0.02 Pg C yr1), mixed forests (0.04 Pg C yr1), and ﬁelds/woods/savannah (0.01 Pg C yr1). The
average sum of the CO2 sink of Chinese forest ecosystems was 0.12 Pg C yr1 (sensitivity experiments range
from 0.09 to 0.19 Pg C yr1) during the studied period. Crop ecosystems in China was estimated as a net
CO2 sink (0.12 Pg C yr1), with a relatively large sensitivity range from 0.09 to 0.26 Pg C yr1. Most of this
crop sink were located in north China, central China, and southeast China. However, this large net crop sink is
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
3506
Journal of Geophysical Research: Atmospheres
10.1002/2013JD021297
Table 3. Chinese Terrestrial A Posteriori Biosphere Fluxes Considered in Ecosystem Types for 2001–2010
Ecosystem Type
Terrestrial Fluxes
1
(Pg C yr )
Flux Total
1
(Pg C yr )
Carbon Sink Strength
2 1
(g C m yr )
Conifer Forest
Broadleaf Forest
Mixed Forest
Fields/Woods/Savanna
Forest/Field
Tropical Forest
0.04
0.02
0.04
0.01
0.00
0.00
0.12
50
Grass/Shrub
Scrub/Woods
Shrub/Tree/Suc.
0.09
0.00
0.00
0.09
35
Crop
Crops
0.12
0.12
71
Other
Semitundra
Northern Taiga
Conifer Snowy/Coastal
Wooded tundra
Mangrove
Wetland
Deserts
Water
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
2
0.33
0.33
33
Category
Forest
Grass/Shrub
All
likely overestimated, and further discussion will be discussed in section 3.4. China’s grassland/shrub CO2 uptake
was 0.09 Pg C yr1 (sensitivity experiments showing the range of 0.09 to 0.17 Pg C yr1) during the period
of 2001–2010, with the highest grass uptake in the eastern edge of Inner Mongolia as expected [Yu et al., 2013].
Detailed comparison on the forests, croplands, and grassland carbon uptake will be discussed in section 3.4.
3.2.2. Interannual Variations in China’s Terrestrial CO2 Flux
Figure 4a shows the interannual variation (IAV) of the Chinese terrestrial ecosystem CO2 ﬂuxes during 2001–
2010. The peak-to-peak amplitude of IAV of Chinese terrestrial ecosystem CO2 ﬂux during 2001–2010 is
0.21 Pg C yr1 (~64% of mean annual average), ranging from 0.19 to 0.40 Pg C yr1. From 2001 to 2010,
the land sink had no obvious trend (R2 = 0.03, p > 0.05, N = 10) but did have a large interannual variability
(coefﬁcient of variation (CV) = 0.25, CV = standard deviation/mean). The sink decreased from 2001 to 2003 at
a rate of 0.08 Pg C yr1, 2004 was a large sink while 2005 was a small sink, followed by a slight increase in sink
size from 2005 to 2009, and 2010 was again a small sink year (Figure 4b).
It is well known that the year-to-year variations in the terrestrial carbon sinks depend on local temperature,
precipitation, and growing season variations [Gurney et al., 2008; Imer et al., 2013; Mohammat et al., 2012;
Peters et al., 2010; Piao et al., 2008; Saeki et al., 2013; Yu et al., 2013]. Figure 5 presents the annual anomalies of
land carbon sink, temperature, and precipitation during 2001–2010. The monthly temperature and
precipitation data were obtained from the China Meteorological Data Sharing service System Administration
(http://cdc.cma.gov.cn/). Our result indicates that the year 2003 was the lowest net CO2 uptake year (0.19
Pg C yr1) in China with a terrestrial uptake which was 0.14 Pg C yr1 lower than the 10 year mean. This is in
agreement with the Chinese crop yield data (derived from National Bureau of Statistics of China, http://data.
stats.gov.cn/index) showing that the year 2003 was the lowest yield year during 2001–2010. This yield
decrease mainly occurred in northeast China, north China, and central China (Figure 6a). In 2003, parts of
China experienced a severe drought, while a heavy ﬂood and a heat wave during the growing season
occurred in other regions of China. At the start of spring in 2003, the terrestrial ecosystem carbon sinks were
strongly reduced due to severe drought over the northeast China (spatial analysis on monthly anomaly,
R2 = 0.10, p < 0.05, N = 240), major part of southwest China (spatial analysis on monthly anomaly, R2 = 0. 03,
p < 0.05, N = 300), and south China (spatial analysis on monthly anomaly, R2 = 0.10, p < 0.05, N = 123). This dry
year was classiﬁed as an “extreme drought” year according to the Palmer Drought stress Index (PDSI < 2) in
the global PDSI data set by Dai [2011a, 2011b] and Dai et al. [2004], available from (http://www.cgd.ucar.edu/
cas/catalog/climind/pdsi.html). Consistent with the 30–80% reduction of precipitation in this area, the spring
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
3507
Journal of Geophysical Research: Atmospheres
(a)
1
(a)
Terrestrial CO2 flux (Pg C)
0.5
0
−0.5
−1
−1.5
−2
−2.5
−3
−3.5
−4
2
4
6
8
10
12
Month
Terrestrial CO2 flux (PgC/yr)
0
(b)
−0.1
−0.2
−0.3
−0.4
−0.5
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
year
Figure 4. (a) The interannual variation of the Chinese terrestrial ecosystem carbon sinks as well as 10 year mean CO2 ﬂux during 2001–2010;
(b) comparison of interannual variation of Chinese terrestrial CO2 ﬂux with
Jiang et al. [2013] and CT2011. The results of CT2011 were obtained from
(ftp://aftp.cmdl.noaa.gov/products/carbontracker/co2/ﬂuxes/monthly/).
10.1002/2013JD021297
carbon uptake (March–May) shows a
signiﬁcant decrease as well (Figure 4a).
During June–August, most of northern
China (such as north China, Inner
Mongolia, and the north corner of the
southwest China, central China, and
southeast China) experienced heavy
ﬂooding due to excessive rainfall [Ju
et al., 2013], while high temperatures
and a drought affected most of
southern China (such as south corner of
the southwest China, central China, and
southeast China) at the same time,
which together diminished the plants
productivity in northern China (spatial
analysis on monthly anomaly, R2 = 0.16,
p < 0.05, N = 597, land sink versus
precipitation) and in southern China
(spatial analysis on monthly anomaly,
R2 = 0.38, p < 0.05, N = 624, land sink
versus temperature) in the summer
growing season (Figure 4a). This
summer carbon uptake deﬁcit
continued through the autumn season
(September–November) with the
prolonged autumn drought in most of
southeast China and southwest China
and led to a low integrated CO2 sink at
the end of the growing season.
Corresponding evidence of the growing
season reduction in terrestrial ecosystem
carbon sinks in 2003 was also measured
at multiple China Flux eddy covariance
sites and reported by other researchers
[e.g., Imer et al., 2013].
In contrast to 2003, the year 2007 is a
relatively high net uptake year (0.40 Pg C yr1) in China, with a modestly increased terrestrial uptake of 0.07
Pg C yr1 relative to the average levels for the analysis period (Figure 6b). In 2007, China had the highest
temperature in the record over the last century [Jiang et al., 2013; NationalClimateCenter, 2008]. Previous
research has suggested many times that warm temperature without moisture deﬁcits could enhance
vegetation growth in spring [Mohammat et al., 2012; Piao et al., 2008; Piao et al., 2007; Wang et al., 2011b]. Our
results are consistent with the previous ﬁndings that the highest CO2 uptake occurred in spring growing
season (March–May) [Mohammat et al., 2012; Piao et al., 2008]. For the summer period, the terrestrial
ecosystem had relatively weak uptake possibly due to the high temperature and uneven distribution of
summer rainfall, making sustained growth enhancements difﬁcult. Warm weather persisted through
December in northeastern China and led to a total increased uptake (spatial analysis on monthly anomaly,
R2 = 0.3, p < 0.05, N = 240) in the Chinese terrestrial ecosystems at the end of 2007 [Ju et al., 2013]. This change
of carbon sink is very different to the anomaly of 2003, and the warm spring/winter combined led to the
largest total terrestrial sink in 2007 over the 10 year period.
The ﬁnding that in the warmest year 2007 (highest temperature over the last century), Chinese terrestrial
ecosystems had a relatively high net uptake (higher than the 10 year average) is arguable, though this is
consistent with the published results based on a bottom-up modeling approach [Ju et al., 2013]. On one hand,
the vegetation photosynthetic activity would be enhanced by warming in the temperature-limited regions, and
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
3508
Journal of Geophysical Research: Atmospheres
Anomaly of land flux (Pg C/yr)
Land sink
Temperature
Precipitation
0
0
Anomaly of climatic factors (%)
10
0.2
−10
−0.2
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Year
Figure 5. The variations of the anomaly of terrestrial carbon sinks and
anomalous percentage of temperature and precipitation in China for
2001–2010. The monthly temperature and precipitation data are
downloaded from China Meteorological Data Sharing service System
Administration (http://cdc.cma.gov.cn/).
10.1002/2013JD021297
this has been evidenced by recent
ﬁndings that the plant growth in cold
regions (e.g., northeastern China) was
primarily constrained by low temperature
in the last two or three decades using the
updated Global Inventory Modeling
and Mapping Studies (GIMMS) third
generation global satellite advanced very
high resolution radiometer normalized
difference vegetation index (NDVI) data
set [e.g., Piao et al., 2011; Chen et al., 2014];
on the other hand, increasing temperature
would increase ecosystem respiration as
well. Whether NEE would increase or
decrease in a warmer year thus depends
on the sensitivities of GPP and Re to
temperature in a given ecosystem.
Chen et al. [2006] reported that boreal
ecosystems sequestered more carbon in
warmer years based on a 13 year hourly atmospheric CO2 record measured on a 40 m tower in northern
Canada. China’s terrestrial ecosystems are characterized by a high diversity, and the majority of ecosystems
are limited by temperature and nutrient availability. In warmer year, the decomposition of soil organic matter
is faster, producing more mineralized nitrogen and other nutrients available for immediate uptake by plant
roots [Braswell et al., 1997; Jarvis et al., 2000]. The comparatively high sink in China in 2007 found in our
inversions might thus be explained by enhancement of GPP by both a direct factor (optimal temperature itself)
and an indirect factor (i.e., warming induced nutrient availability by a faster decomposition of soil organic matter),
causing the increase in GPP to be higher
than the increase in Re. We realize that
(a) 2003 anomalies
this hypothesis needs to be tested using
more lines of evidence from ﬁeld data
and especially the bottom-up carbon
ﬂux studies, which are able to estimate
these two major component CO2 ﬂuxes
of GPP and Re.
(b) 2007 anomalies
Figure 6. The mean ﬂux anomalies (a) in 2003 and (b) in 2007 of this study.
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
The years 2008 and 2009 also showed
high terrestrial ecosystem CO2 uptakes
(Figures 4a and 4b). The year 2008 had
a very normal temperature for most of
China but had plentiful precipitation,
corresponding to increased land sinks
in this year [Jiang et al., 2013]. In
contrast to that in 2008, higher-thanaverage uptake in 2009 was likely
driven by warm temperature. The 2
years of good uptake were followed
by a relatively low uptake year (2010),
which had a high atmospheric CO2
growth rate worldwide (http://www.
esrl.noaa.gov/gmd/ccgg/trends/).
Droughts in the Amazon and in Russia
have been suggested to contribute
most, and recent papers have started
to investigate the impact of summer
3509
Journal of Geophysical Research: Atmospheres
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Table 4. Results From Sensitivity Experiments of Chinese and Asian Additional CO2 Observation (Cases 1 and 2)
a
Described in the Main Text
CO2 Flux
Simulation ID
Case 1
Case 2
Gaussian Error
Prior Flux
1
(Pg C yr )
Post. Flux
1
(Pg C yr )
Flux Difference
1
(Pg C yr )
Prior Error
1
(Pg C yr )
Post. Error
1
(Pg C yr )
Error Reduction
Rate (%)
0.11
0.11
0.29
0.33
0.18
0.23
0.49
0.49
0.38
0.36
22%
27%
a
A priori and posterior ﬂuxes/Gaussian uncertainties as well as their ﬂux difference/error reduction are given for
terrestrial ﬂux in China averaged over 2001–2010.
2010 ﬁres and droughts on the carbon cycle in Asia [Guerlet et al., 2013]. Our system, with its climatological
ﬁres and seasonal biospheres’ uptake for the year 2010, is for now less suited to study this impact, but our results
here suggest that anomalies are probably concentrated more in the Boreal regions than in the more temperate
Chinese region. Similar to that in 2010, the year 2005 is also a low carbon uptake year (the second weakest carbon
uptake year). It had higher-than-average precipitation but lower-than-average temperature, which caused a
decreased carbon sink. In the spring of 2005, there were low temperatures, frost, and snowstorm in most of
northeast China and southern part of China, concurring with decreased carbon uptake. Although strong carbon
uptake in summer season partly diminished some carbon ﬂux anomaly in spring, the annual carbon uptake of
2005 was 0.08 Pg C yr1 lower than the 10 year mean.
3.3. Impact of Additional CO2 Observations
We investigate the impacts of additional CO2 observations (ﬂask data in SDZ, LAN, LFS, and continuous data
in MNM, YON, and GSN) on estimates of Chinese terrestrial ﬂux by comparing the two results from Cases 1
and 2. Table 4 shows the prior and posterior annual mean NEE values, their Gaussian uncertainties as well as
ﬂux difference, and the Gaussian uncertainty reduction between these two experiments. The inferred CO2
ﬂux in Cases 1 and 2 are respectively 0.29 and 0.33 Pg C yr1, which means including extra sites to the
inversion system slightly increased the inferred sink strength over this period.
Figure 7 shows spatial pattern of the ﬂux difference between Cases 1 and 2 during 2001–2010. By including
the additional Chinese and Asian CO2 observation data into the inversion system, the estimated ﬂux
distribution is signiﬁcantly changed. Most negative differences (sink increased when including new sites)
occurred in northeast China, north China, central China, and southeast China, and positive differences (source
increased due to extra sites) occurred in south China. The ﬂux difference in northwest China and Tibetan
Plateau is not so pronounced. Compared to Case 1, the 10 year mean Gaussian uncertainty in Case 2 has a
higher reduction by 5% (see Table 4), which agrees with the (expected) results of Saeki et al. [2013] and
Maksyutov et al. [2003] who also showed that the estimated ﬂux was further constrained by additional
observations. The new observations from China and Asia are therefore important to improve our ability to
estimate spatial patterns in the Chinese terrestrial carbon uptake. We also note that the inﬂuence of the three
extra ﬂask sites in China on the inversion calculation is limited due to high regional source effect. Thus, it is
important to be aware of how well our
model can represent the detailed
locally inﬂuenced observed data.
3.4. Comparison of Our Estimate
With the Other Results
Figure 7. The inverted ﬂux difference between Cases 1 and 2 (Case 2-Case
1) during 2001–2010.
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
A comparison of the inferred terrestrial
CO2 ﬂux of China with previous
inversion results is shown in Table 5.
Our estimated 10 year averaged CO2
sink of Chinese terrestrial ecosystem is
close to the previous results of Piao
et al. [2009] (0.35 ± 0.33 Pg C yr1,
1996–2005 year average), but with
considerable differences in the spatial
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD021297
Table 5. Comparison of the Estimated Carbon Sinks in This Study With Previous Inversion Studies (Pg C yr
1
)
Citation
Area
Carbon Flux
Period
Remarks
This study
China
0.33
2001–2010
China
China
China
China
East Asia
0.29
0.28 ± 0.18
0.35 ± 0.33
0.28
0.38 ± 0.33
2001–2010
2002–2008
1996–2005
2001–2010
1990–2009
Case 2; with extra Chinese and Asian CO2 data;
nested on China
Case 1; nested on China
Nested on China
Nested on North America
Average from 8 inversion models in RECCAP
Jiang et al. [2013]
Piao et al. [2009]
a
CT2011
b
Piao et al. [2012]
a
The
b
results of CT2011 were derived from (ftp://aftp.cmdl.noaa.gov/products/carbontracker/co2/ﬂuxes/monthly/).
East Asia, a region comprised of China, Japan, North and South Korea, and Mongolia.
distribution patterns, especially in northeast China, which is a strong sink in our results but a carbon source in
Piao et al. [2009]. This discrepancy in spatial distribution may be due to large uncertainties in both inverse
models, given the lack of regional constraints over many areas. Compared with the result of Jiang et al. [2013],
who also estimated Chinese CO2 ﬂuxes using an inverse method focusing on China with a similar set of CO2
observations from China (e.g., SDZ, LFS, LAN, and WLG), our estimate of the terrestrial CO2 ﬂux in China
(0.33 Pg C yr1, Case 2) is very similar to that of Jiang et al. [2013] (0.28 ± 0.18 Pg C yr1, 2002–2008 year
average). An interesting difference is that Jiang et al. [2013] used two of these Chinese sites after ﬁtting a
seasonal curve to the original data (the GlobalView approach [Masarie and Tans, 1995]), ﬁltering out all
variations related to the local fossil fuel inﬂuences and then giving much lower MDM to these sites (3.5 ppm).
The similarity of the integrated ﬂuxes gives us conﬁdence that our overall estimate was not signiﬁcantly
biased by using the original, unﬁltered ﬂask CO2 records from these sites near Beijing (SDZ) and the Yangtze
River (LAN). The interannual variations (IAVs) between these two results are also shown in Figure 4b: 2004 was
a weak CO2 sink in Jiang et al. [2013], while 2004 was a higher-than-average carbon sink in this study; 2005
was the smallest sink in Jiang et al. [2013], while 2005 was a larger sink than that in 2003; 2006 and 2009 in this
study were a larger carbon uptake than that in Jiang et al. [2013]; and both studies showed that 2007 was a
relatively strong carbon sink. Moreover, the different inversion methodology between these two approaches
(13 subregions in Jiang et al. [2013] versus 30 ecoregions in this study) would also affect inverted Chinese
results. Table 5 and Figure 4b also present terrestrial CO2 ﬂux from CT2011 (derived from ftp://aftp.cmdl.noaa.
gov/products/carbontracker/co2/ﬂuxes/monthly/), which uses the same inversion framework but focused on
North America with different observations (Also, there are likely more differences: e.g., differences in
biosphere prior/ﬁre ﬂux, different prior uncertainties between CT2011 and Case 2). For 2003 to 2010, our land
sink shows a similar year-to-year variation to the IAV of CT2011, with carbon reduction in 2003, 2005, and
2010 and carbon increase in 2004, 2006, 2007, 2008, and 2009. But we found that most annual carbon uptake
in our estimate is larger than CT2011 during 2005–2009. This is likely due to the extra Chinese and nearby
Asia sites which exert a strong push on our inferred land ﬂuxes and cause an increased carbon sink.
The forest NEE in this study was 0.12 Pg C yr1, which is consistent with other published results (about
0.115 ± 0.05 Pg C yr1) estimated by bottom-up approaches based on inventory data, long-term ﬁeld
observations, and process-based ecosystem models for the period 2000–2007 [e.g., Pan et al., 2011]. Our
results furthermore suggest that the Chinese forest carbon sinks have increased from 2001 to 2010, following
an average trend of 17.7 Tg C yr1 (alternative range from 12.3 to 19.4 Tg C yr1). Although this is consistent
with many previous studies suggesting that China’s national afforestation and reforestation programs have
caused the forest carbon sink to increase [Chen et al., 2013; Pan et al., 2011; Piao et al., 2009; Tian et al., 2011;
Wang et al., 2007], the existence of an increasing sink is still uncertain due to the uncertainty in Chinese fossil
fuel emissions [Francey et al., 2013]. The accumulation rate of carbon per unit area in Chinese forest
ecosystems (50 g C m2 yr1) is still lower than the forest carbon sink strengths of the U.S. (52–71 g C
m2 yr1) and Europe (60–150 g C m2 yr1) [Piao et al., 2009]. This may be related to the different forest
management and production methods in each region. In U.S. and Europe, large forested areas were under
intensive management with regularly thinning, cutting, and regeneration to maintain high productivity [Chen
et al., 2013; Piao et al., 2009]. Such managed forest ecosystems tend to be higher carbon sinks than the large
areas of unmanaged forests in China. Future development of forest management in Chinese forest ecosystem
is thus likely to further increase the forest carbon sink estimated here.
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
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Crop ecosystems in China were estimated to be a net CO2 sink of 0.12 Pg C yr1, with the highest carbon
sink strength of 71 g C m2 yr1. This large strength of crop sink is high because of agricultural practices and
cropping techniques [Chen et al., 2013; Ju et al., 2013; Yu et al., 2013]. In China, cropland is usually under
careful and intensive cultivation to make most efﬁcient use of the relatively small areas of cropland.
Fertilization, watering, and deinsectization techniques are widely used, supplying a comfortable growth
environment for the crops. This in turn causes high productivity of the crops and augments amount of crop
residue to the soil. This could lead to an increased carbon sink in the cropland [Chen et al., 2013].
Multicropping also accounts for a high crop sink in China. Most croplands get two or three crop harvests (e.g.,
rice) a year, especially in central China, southwest China, south China, and southeast China, which have a
signiﬁcant effect on the improvement of the carbon sequestration in cropland [Piao et al., 2009]. However, the
accumulation of our crop carbon is inconsistent with those ﬁndings based on the bottom-up approaches that
crop biomass was considered to have no contribution to a long-term net sink [Fang et al., 2007; Piao et al.,
2009; Tian et al., 2011]. This discrepancy in crop sink can be explained by the lack of crop harvesting and
concurrent consumption in our system. Our atmospheric inversion system can detect strong CO2 uptake
during the crop growing season but cannot see the local emissions of the harvested crop which has been
transported laterally and is consumed elsewhere. CTDAS was not designed to track this lateral carbon
transport. This suggests that our sink in croplands might be overestimated due to the absence of harvesting
in the modeling system. This issue was also raised in Peters et al. [2007, 2010].
Unlike cropland, the carbon uptake rate of shrub/grass is relatively weak (35 g C m2 yr1). This is because the
shrub/grass ecosystems of China were mainly located in arid, semiarid, or alpine regions, which are subject to large
variations in temperature and precipitation [Chen et al., 2013; Ni, 2002] and to high pressure of grazing [Fu et al.,
2009; Yu et al., 2013]. Even though the area of shrub/grass ecosystems accounts for about 30–50% of the total
territory of China [Ni, 2002; Piao et al., 2009], the shrub/grass ﬂux in China was 0.09 Pg C yr1, averaging over the
period of 2001–2010. This estimate is comparable to previously published results of 0.074 Pg C yr1 based on
bottom-up approaches that summer of shrub biomass, grass biomass, shrub soil, and grass soil carbon sinks with
values of 0.022, 0.007, 0.039, and 0.006 Pg C yr1 during 1982–1999, respectively [Piao et al., 2009].
3.5. Uncertainties in Chinese Top-Down CO2 Flux Estimation
Large uncertainties still exist in the Chinese top-down ﬂux estimate presented here. One reason is the limited
number of atmospheric CO2 observations available in China and surrounding countries. Another reason is the
episodically strong impact of local sources on the Chinese sites used [Cheng et al., 2013; Fang et al., 2013],
forcing us to deweight the Chinese time series (see MDM in Table 1) in the assimilation and perhaps not
optimally use their information content when representing typical Chinese background conditions. Regional
stations (e.g., LFS, LAN, and SDZ) in China were previously shown to be affected by local CO2 emissions under
speciﬁc weather regimes [Cheng et al., 2013; Fang et al., 2013; Liu et al., 2009], and we are investigating ways
to conditionally select observations from these locations and thus further maximize their usefulness (and
increase their weight) in our data assimilation effort.
4. Conclusions
We used a regional version of CTDAS focused on China to quantify the weekly NEE of terrestrial ecosystems
over the last decade (2001–2010). Three additional atmospheric CO2 observation sites in China as well as four
sites in Asia were added to the data assimilation system to improve our terrestrial carbon ﬂux estimate. The
results suggest that the Chinese terrestrial ecosystem absorbed a net average of 0.33 Pg C yr1 over the
studied period, compensating approximately 20% of the total CO2 emissions from fossil fuel burning and
cement manufacturing from China. An uncertainty derived from a set of sensitivity experiments suggests the
sink to be in a range of 0.29 to 0.64 Pg C yr1, while a more conservative uncertainty retrieved from the
posterior covariance is ±0.36 Pg C yr1 on this estimate. The spatial distribution of NEE is mostly determined
by the terrestrial ecosystem types, with NEE over forests, croplands, and grass/shrublands representing
carbon sinks of about 0.12, 0.09, and 0.12 Pg C yr1, respectively. The sink in croplands might be
overestimated due to the absence of harvesting in our modeling system. The peak-to-peak amplitude of IAV
of Chinese terrestrial ecosystem carbon ﬂux is 0.21 Pg C yr1, with a range from 0.19 Pg C yr1 to 0.40 Pg C
yr1. The IAV shows that the Chinese CO2 sink is quite sensitive to climate variations, with the largest
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD021297
reduction of uptake in 2003 concurrent with decreased precipitation and extreme drought, while the largest
CO2 sink occurred in 2007 owing to both a warm spring and winter. Increasing forest sinks in China are likely
to contribute to the constancy of the global CO2 growth rather than under increasing fossil fuel emissions,
and their current rate suggests room for further growth of this sink.
Overall, our top-down estimate is in good agreement with independent knowledge on China’s carbon cycle.
Together with increased monitoring capacity, our system will be a valuable tool to further delineate carbon
uptake rates over China in the near future.
Acknowledgments
The data for this paper are available at
(http://www.carbontracker.net/) (under
construction) or you can contact
[email protected] for the
data FTP access. We kindly acknowledge all atmospheric data providers to
the ObsPack version 1.0.2, including the
NOAA Cooperative Air Sampling network and those that contribute their
data to WDCGG. This research was supported by the Strategic Priority
Research Program “Climate Change:
Carbon Budget and Related Issues” of
the Chinese Academy of Sciences (grant
XDA05040403), the National High
Technology Research and Development
Program of China (grant 2013AA122002),
the research grants (41071059 and
41271116) funded by the National
Science Foundation of China, a Research
Plan of LREIS (O88RA900KA), CAS, a
research grant (2012ZD010) of Key
Project for the Strategic Science Plan in
IGSNRR, CAS, and “One Hundred Talents”
program funded by the Chinese
Academy of Sciences. W. Peters was
supported by an NWO VIDI grant
(864.08.012) and the Chinese-Dutch collaboration was funded by the China
Exchange Program project (12CDP006). I.
T. van der Laan-Luijkx has received funding from the European Union’s Seventh
Framework Program (FP7/2007–2013)
under grant agreement 283080, project
GEOCARBON.
ZHANG ET AL.
References
Baker, D., R. Law, K. Gurney, P. Rayner, P. Peylin, A. Denning, P. Bousquet, L. Bruhwiler, Y. H. Chen, and P. Ciais (2006), TransCom 3 inversion
intercomparison: Impact of transport model errors on the interannual variability of regional CO2 ﬂuxes, 1988–2003, Global Biogeochem.
Cycles, 20, GB1002, doi:10.1029/2004GB002439.
Berezin, E. V., I. B. Konovalov, P. Ciais, A. Richter, S. Tao, G. Janssens-Maenhout, M. Beekmann, and E. D. Schulze (2013), Multiannual changes of
CO2 emissions in China: Indirect estimates derived from satellite measurements of tropospheric NO2 columns, Atmos. Chem. Phys., 13(18),
9415–9438, doi:10.5194/acp-13-9415-2013.
Boden, T., G. Marland, and R. Andres (2011), Global, Regional, and National Fossil-Fuel CO2 Emissions, 10 pp., Carbon Dioxide Inf. Anal. Cent.,
Oak Ridge Natl. Lab., U.S. Dep. of Energy, Oak Ridge, Tenn., doi:10.3334/CDIAC/00001_V2011.
Braswell, B. H., D. S. Schimel, E. Linder, and B. Moore (1997), The response of global terrestrial ecosystems to interannual temperature
variability, Science, 278, 870–872.
Bruhwiler, L., A. Michalak, W. Peters, D. Baker, and P. Tans (2005), An improved Kalman Smoother for atmospheric inversions, Atmos. Chem.
Phys., 5(10), 2691–2702.
Cao, M.-K., B. Tao, K.-R. Li, X.-M. Shao, and S. Prience (2003a), Interannual variation in terrestrial ecosystem carbon ﬂuxes in China from 1981 to
1998, Acta Bot. Sin.-Chin. Ed., 45(5), 552–560.
Cao, M., S. D. Prince, K. Li, B. Tao, J. Small, and X. Shao (2003b), Response of terrestrial carbon uptake to climate interannual variability in
China, Global Change Biol., 9(4), 536–546.
Chen, B., et al. (2014), Changes in vegetation photosynthetic activity trends across the Asia-Paciﬁc region over the last three decades, Remote
Sens. Environ., 144, 28–41.
Chen, J. M., B. Chen, K. Higuchi, J. Liu, D. Chan, D. Worthy, P. Tans, and A. Black (2006), Boreal ecosystems sequestered more carbon in warmer
years, Geophys. Res. Lett., 33, L10803, doi:10.1029/2006GL025919.
Chen, Z., G. Yu, J. Ge, X. Sun, T. Hirano, N. Saigusa, Q. Wang, X. Zhu, Y. Zhang, and J. Zhang (2013), Temperature and precipitation control of
the spatial variation of terrestrial ecosystem carbon exchange in the Asian region, Agric. Forest Meteorol., 182–183(0), 266–276,
doi:10.1016/j.agrformet.2013.04.026.
Cheng, Y., X. An, F. Yun, L. Zhou, L. Liu, S. Fang, and L. Xu (2013), Simulation of CO2 variations at Chinese background atmospheric monitoring
stations between 2000 and 2009: Applying a CarbonTracker model, Chin. Sci. Bull., 58, 3986–3993, doi:10.1007/s11434-013-5895-y.
Chevallier, F., and C. W. O’Dell (2013), Error statistics of Bayesian CO2 ﬂux inversion schemes as seen from GOSAT, Geophys. Res. Lett., 40,
1252–1256, doi:10.1002/grl.50228.
Commission, E. (2009), Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL): Emission Database for Global
Atmospheric Research (EDGAR), release version 4.0, edited.
Dai, A. (2011a), Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008, J. Geophys. Res., 116,
D12115, doi:10.1029/2010JD015541.
Dai, A. (2011b), Drought under global warming: A review, Wiley Interdisciplin. Rev. Clim. Change, 2(1), 45–65.
Dai, A., K. E. Trenberth, and T. Qian (2004), A global dataset of Palmer Drought Severity Index for 1870–2002: Relationship with soil moisture
and effects of surface warming, J. Hydrometeorol., 5(6), 1117–1130.
Dee, D. P., et al. (2011), The ERA-Interim reanalysis: Conﬁguration and performance of the data assimilation system, Q. J. R. Meteorol. Soc.,
137(656), 553–597, doi:10.1002/qj.828.
Deng, F., J. M. Chen, M. Ishizawa, C. W. A. I. Yuen, G. Mo, K. Higuchi, D. Chan, and S. Maksyutov (2007), Global monthly CO2 ﬂux inversion with a
focus over North America, Tellus B, 59(2), 179–190.
Fan, Z. M., J. Li, and T. X. Yue (2012), Changes of climate-vegetation ecosystem in Loess Plateau of China, Proc. Environ. Sci., 13(0), 715–720.
Fang, J., Z. Guo, S. Piao, and A. Chen (2007), Terrestrial vegetation carbon sinks in China, 1981–2000, Sci. Chin. Ser. D Earth Sci., 50(9), 1341–1350.
Fang, S., L. Zhou, P. Tans, P. Ciais, M. Steinbacher, L. Xu, and T. Luan (2013), In-situ measurement of atmospheric CO2 at the four WMO/GAW
stations in China, Atmos. Chem. Phys. Discuss., 13(10), 27,287–27,326.
Francey, R. J., et al. (2013), Atmospheric veriﬁcation of anthropogenic CO2 emission trends, Nat. Clim. Change, 3(5), 520–524, doi:10.1038/nclimate1817.
Fu, Y., Z. Zheng, G. Yu, Z. Hu, X. Sun, P. Shi, Y. Wang, and X. Zhao (2009), Environmental inﬂuences on carbon dioxide ﬂuxes over three
grassland ecosystems in China, Biogeosciences, 6(12), 2879–2893.
Gregg, J. S., R. J. Andres, and G. Marland (2008), China: Emissions pattern of the world leader in CO2 emissions from fossil fuel consumption
and cement production, Geophys. Res. Lett., 35, L08806, doi:10.1029/2007GL032887.
Guan, D., G. P. Peters, C. L. Weber, and K. Hubacek (2009), Journey to world top emitter: An analysis of the driving forces of China’s recent CO2
emissions surge, Geophys. Res. Lett., 36, L04709, doi:10.1029/2008GL036540.
Guerlet, S., S. Basu, A. Butz, M. Krol, P. Hahne, S. Houweling, O. P. Hasekamp, and I. Aben (2013), Reduced carbon uptake during the 2010
Northern Hemisphere summer from GOSAT, Geophys. Res. Lett., 40, 2378–2383, doi:10.1002/grl.50402.
Gurney, K. R., R. M. Law, A. S. Denning, P. J. Rayner, D. Baker, P. Bousquet, L. Bruhwiler, Y. H. Chen, P. Ciais, and S. Fan (2002), Towards robust
regional estimates of CO2 sources and sinks using atmospheric transport models, Nature, 415(6872), 626–630.
Gurney, K. R., R. M. Law, A. S. Denning, P. J. Rayner, D. Baker, P. Bousquet, L. Bruhwiler, Y. H. Chen, P. Ciais, and S. Fan (2003), TransCom 3 CO2
inversion intercomparison: 1 Annual mean control results and sensitivity to transport and prior ﬂux information, Tellus B, 55(2), 555–579.
Gurney, K. R., R. M. Law, A. S. Denning, P. J. Rayner, B. C. Pak, D. Baker, P. Bousquet, L. Bruhwiler, Y. H. Chen, and P. Ciais (2004), Transcom 3
inversion intercomparison: Model mean results for the estimation of seasonal carbon sources and sinks, Global Biogeochem. Cycles, 18,
GB1010, doi:10.1029/2003GB002111.
©2014. American Geophysical Union. All Rights Reserved.
3513
Journal of Geophysical Research: Atmospheres
10.1002/2013JD021297
Gurney, K. R., D. Baker, P. Rayner, and S. Denning (2008), Interannual variations in continental-scale net carbon exchange and sensitivity to
observing networks estimated from atmospheric CO2 inversions for the period 1980 to 2005, Global Biogeochem. Cycles, 22, GB3025,
doi:10.1029/2007GB003082.
Houghton, R. (2007), Balancing the global carbon budget, Annu. Rev. Earth Planet. Sci., 35, 313–347.
Imer, D., L. Merbold, W. Eugster, and N. Buchmann (2013), Temporal and spatial variations of CO2, CH4 and N2O ﬂuxes at three differently
managed grasslands, Biogeosciences, 10, 5931–5945, doi:10.5194/bg-10-5931-2013.
Jacobson, A. R., S. E. Mikaloff Fletcher, N. Gruber, J. L. Sarmiento, and M. Gloor (2007), A joint atmosphere-ocean inversion for surface ﬂuxes of
carbon dioxide: 1. Methods and global-scale ﬂuxes, Global Biogeochem. Cycles, 21, GB1019, doi:10.1029/2005GB002556.
Jarvis, P., E. Linder, and S. Botany (2000), Constraints to growth of boreal forests, Nature, 405, 904–905.
Jiang, F., H. W. Wang, J. M. Chen, L. X. Zhou, W. M. Ju, A. J. Ding, L. X. Liu, and W. Peters (2013), Nested atmospheric inversion for the terrestrial
carbon sources and sinks in China, Biogeosciences, 10(8), 5311–5324, doi:10.5194/bg-10-5311-2013.
Ju, H., M. van der Velde, E. Lin, W. Xiong, and Y. Li (2013), The impacts of climate change on agricultural production systems in China, Clim.
Change, 120(1–2), 313–324, doi:10.1007/s10584-013-0803-7.
Krol, M., S. Houweling, B. Bregman, M. Van den Broek, A. Segers, P. Van Velthoven, W. Peters, F. Dentener, and P. Bergamaschi (2005), The two-way
nested global chemistry-transport zoom model TM5: Algorithm and applications, Atmos. Chem. Phys., 5(2), 417–432.
Le Quéré, C., M. R. Raupach, J. G. Canadell, and G. Marland (2009), Trends in the sources and sinks of carbon dioxide, Nat. Geosci., 2(12),
831–836.
Le Quéré, C., et al. (2013), Global carbon budget 2013, Earth Syst. Sci. Data Discuss., 6(2), 689–760, doi:10.5194/essdd-6-689-2013.
Liu, L., L. Zhou, X. Zhang, M. Wen, F. Zhang, B. Yao, and S. Fang (2009), The characteristics of atmospheric CO2 concentration variation of four
national background stations in China, Sci. Chin. Ser. D Earth Sci., 52(11), 1857–1863, doi:10.1007/s11430-009-0143-7.
Liu, S., T. Zhou, L. Wei, and Y. Shu (2012), The spatial distribution of forest carbon sinks and sources in China, Chin. Sci. Bull., 57(14), 1699–1707.
Lun, F., W. Li, and Y. Liu (2012), Complete forest carbon cycle and budget in China, 1999–2008, Forest Ecol Manage, 264, 81–89.
Maksyutov, S., T. Machida, H. Mukai, P. K. Patra, T. Nakazawa, and G. Inoue (2003), Effect of recent observations on Asian CO2 ﬂux estimates by
transport model inversions, Tellus B, 55(2), 522–529.
Marland, G., T. A. Boden, R. J. Andres, A. Brenkert, and C. Johnston (2003), Global, regional, and national fossil fuel CO2 emissions, in
Trends: A Compendium of Data on Global Change, pp. 34–43, Carbon Dioxide Inf. Anal. Cent., Environ. Sci. Div., Oak Ridge Natl. Lab.,
Oak Ridge, Tenn.
Masarie, K. A., and P. P. Tans (1995), Extension and integration of atmospheric carbon dioxide data into a globally consistent measurement
record, J. Geophys. Res., 100(D6), 11,593–11,610.
Michalak, A. M., A. Hirsch, L. Bruhwiler, K. R. Gurney, W. Peters, and P. P. Tans (2005), Maximum likelihood estimation of covariance parameters
for Bayesian atmospheric trace gas surface ﬂux inversions, J. Geophys. Res., 110, D24107, doi:10.1029/2005JD005970.
Mohammat, A., X. Wang, X. Xu, L. Peng, Y. Yang, X. Zhang, R. B. Myneni, and S. Piao (2012), Drought and spring cooling induced recent
decrease in vegetation growth in Inner Asia, Agric. Forest Meteorol., 178–179, 21–30.
NationalClimateCenter (2008), 2007 China Climate Bulletin, Chin. Meteorol. Press, 1, 40.
Ni, J. (2002), Carbon storage in grasslands of China, J. Arid Environ., 50(2), 205–218.
Olsen, S. C., and J. T. Randerson (2004), Differences between surface and column atmospheric CO2 and implications for carbon cycle
research, J. Geophys. Res., 109, D02301, doi:10.1029/2003JD003968.
Olson, J. S., J. A. Watts, and L. J. Allison (1985), Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: A Database, NDP-017,
Oak Ridge Natl. Lab., Oak Ridge, Tenn.
Pan, Y., et al. (2011), A large and persistent carbon sink in the world’s forests, Science, 333(6045), 988–993, doi:10.1126/science.1201609.
Peters, W., J. Miller, J. Whitaker, A. Denning, A. Hirsch, M. Krol, D. Zupanski, L. Bruhwiler, and P. Tans (2005), An ensemble data assimilation
system to estimate CO2 surface ﬂuxes from atmospheric trace gas observations, J. Geophys. Res., 110, D24304, doi:10.1029/2005JD006157.
Peters, W., A. R. Jacobson, C. Sweeney, A. E. Andrews, T. J. Conway, K. Masarie, J. B. Miller, L. M. P. Bruhwiler, G. Petron, and A. I. Hirsch (2007), An
atmospheric perspective on North American carbon dioxide exchange: CarbonTracker, Proc. Natl. Acad. Sci. U. S. A., 104(48), 18,925–18,930.
Peters, W., M. Krol, G. Van der Werf, S. Houweling, C. Jones, J. Hughes, K. Schaefer, K. Masarie, A. Jacobson, and J. Miller (2010), Seven years of
recent European net terrestrial carbon dioxide exchange constrained by atmospheric observations, Global Change Biol., 16(4), 1317–1337.
Peters, G. P., G. Marland, C. Le Quéré, T. Boden, J. G. Canadell, and M. R. Raupach (2011), Rapid growth in CO2 emissions after the 2008–2009
global ﬁnancial crisis, Nat. Clim. Change, 2, 2–4, doi:10.1038/nclimate1332.
Peylin, P., R. Law, K. Gurney, F. Chevallier, A. Jacobson, T. Maki, Y. Niwa, P. Patra, W. Peters, and P. Rayner (2013), Global atmospheric carbon
budget: Results from an ensemble of atmospheric CO2 inversions, Biogeosci. Discuss., 10(3), 5301–5360.
Piao, S. L., P. Friedlingstein, P. Ciais, N. Viovy, and J. Demarty (2007), Growing season extension and its impact on terrestrial carbon cycle in
the Northern Hemisphere over the past 2 decades, Global Biogeochem. Cycles, 21, GB3018, doi:10.1029/2006GB002888.
Piao, S., P. Ciais, P. Friedlingstein, P. Peylin, M. Reichstein, S. Luyssaert, H. Margolis, J. Fang, A. Barr, and A. Chen (2008), Net carbon dioxide
losses of northern ecosystems in response to autumn warming, Nature, 451(7174), 49–52.
Piao, S., J. Fang, P. Ciais, P. Peylin, Y. Huang, S. Sitch, and T. Wang (2009), The carbon balance of terrestrial ecosystems in China, Nature,
458(7241), 1009–1013.
Piao, S., X. Wang, P. Ciais, B. Zhu, T. Wang, and J. Liu (2011), Changes in satellite-derived vegetation growth trend in temperate and boreal
Eurasia from 1982 to 2006, Global Change Biol., 17, 3228–3239.
Piao, S. L., et al. (2012), The carbon budget of terrestrial ecosystems in East Asia over the last two decades, Biogeosciences, 9(9), 3571–3586,
doi:10.5194/bg-9-3571-2012.
Saeki, T., S. Maksyutov, M. Sasakawa, T. Machida, M. Arshinov, P. Tans, T. Conway, M. Saito, V. Valsala, and T. Oda (2013), Carbon ﬂux estimation
for Siberia by inverse modeling constrained by aircraft and tower CO2 measurements, J. Geophys. Res. Atmos., 118, 1100–1122,
doi:10.1002/jgrd.50127.
Schaefer, K., G. J. Collatz, P. Tans, A. S. Denning, I. Baker, J. Berry, L. Prihodko, N. Suits, and A. Philpott (2008), Combined Simple Biosphere/
Carnegie-Ames-Stanford Approach terrestrial carbon cycle model, J. Geophys. Res., 113, G03034, doi:10.1029/2007jg000603.
Stephens, B. B., K. R. Gurney, P. P. Tans, C. Sweeney, W. Peters, L. Bruhwiler, P. Ciais, M. Ramonet, P. Bousquet, and T. Nakazawa (2007), Weak
northern and strong tropical land carbon uptake from vertical proﬁles of atmospheric CO2, Science, 316(5832), 1732–1735.
Thoning, K. W., P. P. Tans, and W. D. Komhyr (1989), Atmospheric carbon dioxide at Mauna Loa Observatory: 2. Analysis of the NOAA GMCC
data, 1974–1985, J. Geophys. Res., 94(D6), 8549–8565.
Tian, H., J. Melillo, C. Lu, D. Kicklighter, M. Liu, W. Ren, X. Xu, G. Chen, C. Zhang, and S. Pan (2011), Chinas terrestrial carbon balance:
Contributions from multiple global change factors, Global Biogeochem. Cycles, 25, GB1007, doi:10.1029/2010GB003838.
Wang, H., R. Zhang, M. Liu, and J. Bi (2012), The carbon emissions of Chinese cities, Atmos. Chem. Phys., 12(14), 6197–6206.
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
3514
Journal of Geophysical Research: Atmospheres
10.1002/2013JD021297
Wang, M. N., H. Wang, D. Y. Qin, C. Y. Lu, and Y. P. Li (2011a), Modelling the artiﬁcial recharge of a wetland and its inﬂuence on regional
hydrological process in China: A case study, Ecohydrology, 4(4), 589–596, doi:10.1002/Eco.175.
Wang, S., J. Chen, W. Ju, X. Feng, M. Chen, P. Chen, and G. Yu (2007), Carbon sinks and sources in Chinas forests during 1901–2001, J. Environ.
Manage., 85(3), 524–537.
Wang, X., S. Piao, P. Ciais, J. Li, P. Friedlingstein, C. Koven, and A. Chen (2011b), Spring temperature change and its implication in the change
of vegetation growth in North America from 1982 to 2006, Proc. Natl. Acad. Sci. U. S. A., 108(4), 1240–1245.
Werf, G., J. T. Randerson, L. Giglio, G. J. Collatz, P. S. Kasibhatla, and A. Arellano Jr. (2006), Interannual variability in global biomass burning
emissions from 1997 to 2004, Atmos. Chem. Phys., 6(11), 3423–3441.
Yang, Z., R. Washenfelder, G. Keppel-Aleks, N. Krakauer, J. Randerson, P. Tans, C. Sweeney, and P. Wennberg (2007), New constraints on
Northern Hemisphere growing season net ﬂux, Geophys. Res. Lett., 34, L12807, doi:10.1029/2007GL029742.
Yu, G. R., X. J. Zhu, Y. L. Fu, H. L. He, Q. F. Wang, X. F. Wen, X. R. Li, L. M. Zhang, L. Zhang, and W. Su (2013), Spatial patterns and climate drivers of
carbon ﬂuxes in terrestrial ecosystems of China, Global Change Biol., 19(3), 798–810.
Zhang, H., B. Chen, I. van der Laan-Luijkx, T. Machida, H. Matsueda, Y. Sawa, Y. Fukuyama, C. Labuschagne, R. Langenfelds, and M. van der Schoot
(2013), Estimating Asian terrestrial carbon ﬂuxes from CONTRAIL aircraft and surface CO2 observations for the period 2006 to 2010, Atmos.
Chem. Phys. Discuss., 13(10), 27,597–27,639.
ZHANG ET AL.
©2014. American Geophysical Union. All Rights Reserved.
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